dorsal/arxiv
View SchemaOptimal signal processing in small stochastic biochemical networks
| Authors | Etay Ziv, Ilya Nemenman, Chris H. Wiggins |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0612041 |
| URL | https://arxiv.org/abs/q-bio/0612041 |
| DOI | 10.1371/journal.pone.0001077 |
Abstract
We quantify the influence of the topology of a transcriptional regulatory network on its ability to process environmental signals. By posing the problem in terms of information theory, we may do this without specifying the function performed by the network. Specifically, we study the maximum mutual information between the input (chemical) signal and the output (genetic) response attainable by the network in the context of an analytic model of particle number fluctuations. We perform this analysis for all biochemical circuits, including various feedback loops, that can be built out of 3 chemical species, each under the control of one regulator. We find that a generic network, constrained to low molecule numbers and reasonable response times, can transduce more information than a simple binary switch and, in fact, manages to achieve close to the optimal information transmission fidelity. These high-information solutions are robust to tenfold changes in most of the networks' biochemical parameters; moreover they are easier to achieve in networks containing cycles with an odd number of negative regulators (overall negative feedback) due to their decreased molecular noise (a result which we derive analytically). Finally, we demonstrate that a single circuit can support multiple high-information solutions. These findings suggest a potential resolution of the "cross-talk" dilemma as well as the previously unexplained observation that transcription factors which undergo proteolysis are more likely to be auto-repressive.
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"abstract": "We quantify the influence of the topology of a transcriptional regulatory\nnetwork on its ability to process environmental signals. By posing the problem\nin terms of information theory, we may do this without specifying the function\nperformed by the network. Specifically, we study the maximum mutual information\nbetween the input (chemical) signal and the output (genetic) response\nattainable by the network in the context of an analytic model of particle\nnumber fluctuations. We perform this analysis for all biochemical circuits,\nincluding various feedback loops, that can be built out of 3 chemical species,\neach under the control of one regulator. We find that a generic network,\nconstrained to low molecule numbers and reasonable response times, can\ntransduce more information than a simple binary switch and, in fact, manages to\nachieve close to the optimal information transmission fidelity. These\nhigh-information solutions are robust to tenfold changes in most of the\nnetworks\u0027 biochemical parameters; moreover they are easier to achieve in\nnetworks containing cycles with an odd number of negative regulators (overall\nnegative feedback) due to their decreased molecular noise (a result which we\nderive analytically). Finally, we demonstrate that a single circuit can support\nmultiple high-information solutions. These findings suggest a potential\nresolution of the \"cross-talk\" dilemma as well as the previously unexplained\nobservation that transcription factors which undergo proteolysis are more\nlikely to be auto-repressive.",
"arxiv_id": "q-bio/0612041",
"authors": [
"Etay Ziv",
"Ilya Nemenman",
"Chris H. Wiggins"
],
"categories": [
"q-bio.MN",
"q-bio.QM"
],
"doi": "10.1371/journal.pone.0001077",
"title": "Optimal signal processing in small stochastic biochemical networks",
"url": "https://arxiv.org/abs/q-bio/0612041"
},
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